We denote the content image by \(I\) , the style image by \(S\)  and the stylized image (output) by \(\tilde{I}\) . Let \(f_l(x)\)  be the feature map of layer \(l\)  with \(x\)  being the input to the pretrained loss network, specifically, either \(I\)  or \(\tilde{I}\) . For simplicity, batch size is set to 1. Then, a typical neural network feature map has a 3D shape \(C_l\times H_l\times W_l\) , where \(C_l\)  denotes the number of channels, and \(H_l\)  and \(W_l\)  denote the height and width. The feature map \(f_l(x)\)  can be flattened into a 2D matrix \(F_l(x)\)  with the shape \(C_l \times H_l W_l\) . The Gram matrix of \(f_l(x)\)  is defined as [1]}:
\(G_l(x) = f_l(x) f_l(x)^\top .\) 
